SHARPpy: An Open-Source Sounding Analysis Toolkit for the Atmospheric Sciences

AbstractWith a variety of programming languages and data formats available, widespread adoption of computing standards by the atmospheric science community is often difficult to achieve. The Sounding and Hodograph Analysis and Research Program in Python (SHARPpy) is an open-source, cross-platform, upper-air sounding analysis and visualization package. SHARPpy is based on the National Oceanic and Atmospheric Administration/Storm Prediction Center’s (NOAA/SPC) in-house analysis package, SHARP, and is the result of a collaborative effort between forecasters at the SPC and students at the University of Oklahoma’s School of Meteorology. The major aim of SHARPpy is to provide a consistent framework for sounding analysis that is available to all. Nearly all routines are written to be as consistent as possible with the methods researched, tested, and developed in the SPC, which sets this package apart from other sounding analysis tools.SHARPpy was initially demonstrated and released to the atmospheric community a...

[1]  Harold E. Brooks,et al.  Discriminating between Tornadic and Nontornadic Thunderstorms Using Mesoscale Model Output , 1997 .

[2]  Charles A. Doswell,et al.  The Cool-Season Tornadoes of California and Southern Australia , 2002 .

[3]  John M. Esterheld,et al.  Discriminating between Tornadic and Non-Tornadic Supercells: A New Hodograph Technique , 2008, E-Journal of Severe Storms Meteorology.

[4]  Patrick Minnis,et al.  Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part II: Combined Radar and Satellite Data Experiments , 2016 .

[5]  Richard L. Thompson,et al.  Effective Storm-Relative Helicity and Bulk Shear in Supercell Thunderstorm Environments , 2007 .

[6]  Patrick T. Marsh,et al.  Diagnosing the Conditional Probability of Tornado Damage Rating Using Environmental and Radar Attributes , 2015 .

[7]  Richard L. Thompson,et al.  Eta Model storm-relative winds associated with tornadic and nontornadic supercells , 1998 .

[8]  Erik N. Rasmussen,et al.  The Effect of Neglecting the Virtual Temperature Correction on CAPE Calculations , 1994 .

[9]  Richard L. Thompson,et al.  Close Proximity Soundings within Supercell Environments Obtained from the Rapid Update Cycle , 2003 .

[10]  Stanley G. Benjamin,et al.  CONVECTIVE-SCALE WARN-ON-FORECAST SYSTEM: A vision for 2020 , 2009 .

[11]  P. Bourgouin.  A Method to Determine Precipitation Types , 2000 .

[12]  Erik N. Rasmussen,et al.  Variations in Supercell Morphology. Part I: Observations of the Role of Upper-Level Storm-Relative Flow , 1998 .

[13]  Qingyang Xiao,et al.  The Performance of the National Weather Service Heat Warning System against Ground Observations and Satellite Imagery , 2015 .

[14]  David J. Stensrud,et al.  Effects of Upper-Level Shear on the Structure and Maintenance of Strong Quasi-Linear Mesoscale Convective Systems , 2006 .

[15]  Matthew S. Gilmore,et al.  The Influence of Midtropospheric Dryness on Supercell Morphology and Evolution , 1998 .

[16]  Robert Davies-Jones,et al.  An Efficient and Accurate Method for Computing the Wet-Bulb Temperature along Pseudoadiabats , 2008 .

[17]  Richard L. Thompson,et al.  Convective Modes for Significant Severe Thunderstorms in the Contiguous United States. Part II: Supercell and QLCS Tornado Environments , 2012 .

[18]  Stephen F. Corfidi,et al.  Cold Pools and MCS Propagation: Forecasting the Motion of Downwind-Developing MCSs , 2003 .

[19]  David D. Turner,et al.  The 2015 Plains Elevated Convection at Night Field Project , 2017 .

[20]  Silas Michaelides,et al.  Meteorological conditions contributing to the development of severe tornadoes in southern Cyprus , 2006 .

[21]  J. Fritsch,et al.  Predicting the Movement of Mesoscale Convective Complexes , 1996 .

[22]  Erik N. Rasmussen,et al.  Refined Supercell and Tornado Forecast Parameters , 2003 .

[23]  Thomas A. Jones,et al.  Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar Data Experiments , 2015 .

[24]  Damien Irving,et al.  A Minimum Standard for Publishing Computational Results in the Weather and Climate Sciences , 2016 .

[25]  Kathryn R. Fossell,et al.  NCAR’s Experimental Real-Time Convection-Allowing Ensemble Prediction System , 2015 .

[26]  Johnny Wei-Bing Lin.  Why Python Is the Next Wave in Earth Sciences Computing , 2012 .

[27]  Charles A. Doswell.  Forecaster Workstation Design: Concepts and Issues , 1992 .

[28]  Jonathan M. Davies.  Tornadoes with Cold Core 500-mb Lows , 2006 .

[29]  Richard L. Thompson,et al.  Choosing a Universal Mean Wind for Supercell Motion Prediction , 2014 .

[30]  Richard L. Thompson,et al.  Predicting Supercell Motion Using a New Hodograph Technique , 2000 .

[31]  Matthew J. Bunkers,et al.  An Observational Examination of Long-Lived Supercells. Part II: Environmental Conditions and Forecasting , 2006 .

[32]  Steven A. Ackerman,et al.  Comparison of Satellite-, Model-, and Radiosonde-Derived Convective Available Potential Energy in the Southern Great Plains Region , 2017 .

[33]  E. Rasmussen,et al.  A Baseline Climatology of Sounding-Derived Supercell and Tornado Forecast Parameters , 1998 .

[34]  Richard L. Thompson,et al.  Short-Term Convective Mode Evolution along Synoptic Boundaries , 2010 .